Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

Wojciech Nazar; Stanisław Szymanowicz; Krzysztof Nazar; Damian Kaufmann; Elżbieta Wabich; Rüdiger Braun‑Dullaeus; Ludmiła Daniłowicz‑Szymanowicz

doi:https://doi.org/10.1007/s10741-023-10357-8

Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

Abstrakt

The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based …